Overview

Dataset statistics

Number of variables22
Number of observations8101
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.4 MiB
Average record size in memory176.0 B

Variable types

Numeric16
Categorical6

Alerts

Customer_Age is highly overall correlated with Months_on_bookHigh correlation
Months_on_book is highly overall correlated with Customer_AgeHigh correlation
Credit_Limit is highly overall correlated with Avg_Open_To_BuyHigh correlation
Total_Revolving_Bal is highly overall correlated with Avg_Utilization_RatioHigh correlation
Avg_Open_To_Buy is highly overall correlated with Credit_Limit and 1 other fieldsHigh correlation
Total_Trans_Amt is highly overall correlated with Total_Trans_CtHigh correlation
Total_Trans_Ct is highly overall correlated with Total_Trans_AmtHigh correlation
Avg_Utilization_Ratio is highly overall correlated with Total_Revolving_Bal and 1 other fieldsHigh correlation
Gender is highly overall correlated with Income_CategoryHigh correlation
Income_Category is highly overall correlated with GenderHigh correlation
Card_Category is highly imbalanced (79.4%)Imbalance
train_idx is uniformly distributedUniform
train_idx has unique valuesUnique
CLIENTNUM has unique valuesUnique
Dependent_count has 725 (8.9%) zerosZeros
Contacts_Count_12_mon has 312 (3.9%) zerosZeros
Total_Revolving_Bal has 1986 (24.5%) zerosZeros
Avg_Utilization_Ratio has 1986 (24.5%) zerosZeros

Reproduction

Analysis started2023-04-12 09:42:11.227428
Analysis finished2023-04-12 09:42:55.819970
Duration44.59 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

train_idx
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct8101
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4050
Minimum0
Maximum8100
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size63.4 KiB
2023-04-12T11:42:55.953705image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile405
Q12025
median4050
Q36075
95-th percentile7695
Maximum8100
Range8100
Interquartile range (IQR)4050

Descriptive statistics

Standard deviation2338.7016
Coefficient of variation (CV)0.57745718
Kurtosis-1.2
Mean4050
Median Absolute Deviation (MAD)2025
Skewness0
Sum32809050
Variance5469525.2
MonotonicityStrictly increasing
2023-04-12T11:42:56.151026image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
5396 1
 
< 0.1%
5409 1
 
< 0.1%
5408 1
 
< 0.1%
5407 1
 
< 0.1%
5406 1
 
< 0.1%
5405 1
 
< 0.1%
5404 1
 
< 0.1%
5403 1
 
< 0.1%
5402 1
 
< 0.1%
Other values (8091) 8091
99.9%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
8100 1
< 0.1%
8099 1
< 0.1%
8098 1
< 0.1%
8097 1
< 0.1%
8096 1
< 0.1%
8095 1
< 0.1%
8094 1
< 0.1%
8093 1
< 0.1%
8092 1
< 0.1%
8091 1
< 0.1%

CLIENTNUM
Real number (ℝ)

Distinct8101
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.3913295 × 108
Minimum7.0808208 × 108
Maximum8.2834308 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size63.4 KiB
2023-04-12T11:42:56.347682image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum7.0808208 × 108
5-th percentile7.0910126 × 108
Q17.1305338 × 108
median7.1788601 × 108
Q37.7284638 × 108
95-th percentile8.1397128 × 108
Maximum8.2834308 × 108
Range1.20261 × 108
Interquartile range (IQR)59793000

Descriptive statistics

Standard deviation36919116
Coefficient of variation (CV)0.049949222
Kurtosis-0.60777433
Mean7.3913295 × 108
Median Absolute Deviation (MAD)6292200
Skewness1.0005322
Sum5.987716 × 1012
Variance1.3630211 × 1015
MonotonicityNot monotonic
2023-04-12T11:42:56.536297image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
713071383 1
 
< 0.1%
759947433 1
 
< 0.1%
709841433 1
 
< 0.1%
789483333 1
 
< 0.1%
785328408 1
 
< 0.1%
805534608 1
 
< 0.1%
709005633 1
 
< 0.1%
718246458 1
 
< 0.1%
789917208 1
 
< 0.1%
778377183 1
 
< 0.1%
Other values (8091) 8091
99.9%
ValueCountFrequency (%)
708082083 1
< 0.1%
708083283 1
< 0.1%
708084558 1
< 0.1%
708085458 1
< 0.1%
708086958 1
< 0.1%
708098133 1
< 0.1%
708099183 1
< 0.1%
708100533 1
< 0.1%
708103608 1
< 0.1%
708104658 1
< 0.1%
ValueCountFrequency (%)
828343083 1
< 0.1%
828298908 1
< 0.1%
828294933 1
< 0.1%
828288333 1
< 0.1%
828285858 1
< 0.1%
828281733 1
< 0.1%
828236133 1
< 0.1%
828227433 1
< 0.1%
828215508 1
< 0.1%
827984658 1
< 0.1%

Customer_Age
Real number (ℝ)

Distinct44
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.306382
Minimum26
Maximum70
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size63.4 KiB
2023-04-12T11:42:56.724859image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum26
5-th percentile33
Q141
median46
Q352
95-th percentile60
Maximum70
Range44
Interquartile range (IQR)11

Descriptive statistics

Standard deviation8.0225266
Coefficient of variation (CV)0.17324883
Kurtosis-0.31128146
Mean46.306382
Median Absolute Deviation (MAD)6
Skewness-0.043812536
Sum375128
Variance64.360933
MonotonicityNot monotonic
2023-04-12T11:42:56.890904image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
46 397
 
4.9%
49 395
 
4.9%
45 390
 
4.8%
44 386
 
4.8%
47 378
 
4.7%
48 372
 
4.6%
43 371
 
4.6%
50 367
 
4.5%
42 326
 
4.0%
51 325
 
4.0%
Other values (34) 4394
54.2%
ValueCountFrequency (%)
26 63
0.8%
27 22
 
0.3%
28 24
 
0.3%
29 50
 
0.6%
30 53
 
0.7%
31 78
1.0%
32 90
1.1%
33 98
1.2%
34 126
1.6%
35 143
1.8%
ValueCountFrequency (%)
70 1
 
< 0.1%
68 2
 
< 0.1%
67 3
 
< 0.1%
66 1
 
< 0.1%
65 77
1.0%
64 32
 
0.4%
63 58
0.7%
62 70
0.9%
61 73
0.9%
60 99
1.2%

Gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size63.4 KiB
F
4279 
M
3822 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8101
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowF
2nd rowF
3rd rowF
4th rowF
5th rowF

Common Values

ValueCountFrequency (%)
F 4279
52.8%
M 3822
47.2%

Length

2023-04-12T11:42:57.032693image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-12T11:42:57.195216image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
f 4279
52.8%
m 3822
47.2%

Most occurring characters

ValueCountFrequency (%)
F 4279
52.8%
M 3822
47.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 8101
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
F 4279
52.8%
M 3822
47.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 8101
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
F 4279
52.8%
M 3822
47.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8101
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
F 4279
52.8%
M 3822
47.2%

Dependent_count
Real number (ℝ)

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3347735
Minimum0
Maximum5
Zeros725
Zeros (%)8.9%
Negative0
Negative (%)0.0%
Memory size63.4 KiB
2023-04-12T11:42:57.448655image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2895637
Coefficient of variation (CV)0.55232927
Kurtosis-0.65676736
Mean2.3347735
Median Absolute Deviation (MAD)1
Skewness-0.020167501
Sum18914
Variance1.6629746
MonotonicityNot monotonic
2023-04-12T11:42:57.565090image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 2222
27.4%
2 2150
26.5%
1 1465
18.1%
4 1212
15.0%
0 725
 
8.9%
5 327
 
4.0%
ValueCountFrequency (%)
0 725
 
8.9%
1 1465
18.1%
2 2150
26.5%
3 2222
27.4%
4 1212
15.0%
5 327
 
4.0%
ValueCountFrequency (%)
5 327
 
4.0%
4 1212
15.0%
3 2222
27.4%
2 2150
26.5%
1 1465
18.1%
0 725
 
8.9%

Education_Level
Categorical

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size63.4 KiB
Graduate
2528 
High School
1619 
Unknown
1205 
Uneducated
1171 
College
816 
Other values (2)
762 

Length

Max length13
Median length11
Mean length8.9342057
Min length7

Characters and Unicode

Total characters72376
Distinct characters25
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUnknown
2nd rowHigh School
3rd rowUnknown
4th rowGraduate
5th rowHigh School

Common Values

ValueCountFrequency (%)
Graduate 2528
31.2%
High School 1619
20.0%
Unknown 1205
14.9%
Uneducated 1171
14.5%
College 816
 
10.1%
Post-Graduate 407
 
5.0%
Doctorate 355
 
4.4%

Length

2023-04-12T11:42:57.728789image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-12T11:42:57.920643image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
graduate 2528
26.0%
high 1619
16.7%
school 1619
16.7%
unknown 1205
12.4%
uneducated 1171
12.0%
college 816
 
8.4%
post-graduate 407
 
4.2%
doctorate 355
 
3.7%

Most occurring characters

ValueCountFrequency (%)
a 7396
 
10.2%
e 7264
 
10.0%
o 6376
 
8.8%
d 5277
 
7.3%
t 5223
 
7.2%
n 4786
 
6.6%
u 4106
 
5.7%
r 3290
 
4.5%
l 3251
 
4.5%
h 3238
 
4.5%
Other values (15) 22169
30.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 60223
83.2%
Uppercase Letter 10127
 
14.0%
Space Separator 1619
 
2.2%
Dash Punctuation 407
 
0.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 7396
12.3%
e 7264
12.1%
o 6376
10.6%
d 5277
8.8%
t 5223
8.7%
n 4786
7.9%
u 4106
6.8%
r 3290
 
5.5%
l 3251
 
5.4%
h 3238
 
5.4%
Other values (6) 10016
16.6%
Uppercase Letter
ValueCountFrequency (%)
G 2935
29.0%
U 2376
23.5%
S 1619
16.0%
H 1619
16.0%
C 816
 
8.1%
P 407
 
4.0%
D 355
 
3.5%
Space Separator
ValueCountFrequency (%)
1619
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 407
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 70350
97.2%
Common 2026
 
2.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 7396
 
10.5%
e 7264
 
10.3%
o 6376
 
9.1%
d 5277
 
7.5%
t 5223
 
7.4%
n 4786
 
6.8%
u 4106
 
5.8%
r 3290
 
4.7%
l 3251
 
4.6%
h 3238
 
4.6%
Other values (13) 20143
28.6%
Common
ValueCountFrequency (%)
1619
79.9%
- 407
 
20.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 72376
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 7396
 
10.2%
e 7264
 
10.0%
o 6376
 
8.8%
d 5277
 
7.3%
t 5223
 
7.2%
n 4786
 
6.6%
u 4106
 
5.7%
r 3290
 
4.5%
l 3251
 
4.5%
h 3238
 
4.5%
Other values (15) 22169
30.6%

Marital_Status
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size63.4 KiB
Married
3767 
Single
3144 
Divorced
611 
Unknown
579 

Length

Max length8
Median length7
Mean length6.6873226
Min length6

Characters and Unicode

Total characters54174
Distinct characters17
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSingle
2nd rowMarried
3rd rowSingle
4th rowSingle
5th rowMarried

Common Values

ValueCountFrequency (%)
Married 3767
46.5%
Single 3144
38.8%
Divorced 611
 
7.5%
Unknown 579
 
7.1%

Length

2023-04-12T11:42:58.102527image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-12T11:42:58.283952image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
married 3767
46.5%
single 3144
38.8%
divorced 611
 
7.5%
unknown 579
 
7.1%

Most occurring characters

ValueCountFrequency (%)
r 8145
15.0%
i 7522
13.9%
e 7522
13.9%
n 4881
9.0%
d 4378
8.1%
M 3767
7.0%
a 3767
7.0%
l 3144
 
5.8%
g 3144
 
5.8%
S 3144
 
5.8%
Other values (7) 4760
8.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 46073
85.0%
Uppercase Letter 8101
 
15.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 8145
17.7%
i 7522
16.3%
e 7522
16.3%
n 4881
10.6%
d 4378
9.5%
a 3767
8.2%
l 3144
 
6.8%
g 3144
 
6.8%
o 1190
 
2.6%
v 611
 
1.3%
Other values (3) 1769
 
3.8%
Uppercase Letter
ValueCountFrequency (%)
M 3767
46.5%
S 3144
38.8%
D 611
 
7.5%
U 579
 
7.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 54174
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 8145
15.0%
i 7522
13.9%
e 7522
13.9%
n 4881
9.0%
d 4378
8.1%
M 3767
7.0%
a 3767
7.0%
l 3144
 
5.8%
g 3144
 
5.8%
S 3144
 
5.8%
Other values (7) 4760
8.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 54174
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 8145
15.0%
i 7522
13.9%
e 7522
13.9%
n 4881
9.0%
d 4378
8.1%
M 3767
7.0%
a 3767
7.0%
l 3144
 
5.8%
g 3144
 
5.8%
S 3144
 
5.8%
Other values (7) 4760
8.8%

Income_Category
Categorical

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size63.4 KiB
Less than $40K
2812 
$40K - $60K
1453 
$80K - $120K
1237 
$60K - $80K
1122 
Unknown
889 

Length

Max length14
Median length12
Mean length11.464757
Min length7

Characters and Unicode

Total characters92876
Distinct characters22
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUnknown
2nd rowUnknown
3rd rowLess than $40K
4th rowLess than $40K
5th row$40K - $60K

Common Values

ValueCountFrequency (%)
Less than $40K 2812
34.7%
$40K - $60K 1453
17.9%
$80K - $120K 1237
15.3%
$60K - $80K 1122
 
13.9%
Unknown 889
 
11.0%
$120K + 588
 
7.3%

Length

2023-04-12T11:42:58.427781image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-12T11:42:58.784243image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
4400
20.1%
40k 4265
19.4%
less 2812
12.8%
than 2812
12.8%
60k 2575
11.7%
80k 2359
10.8%
120k 1825
8.3%
unknown 889
 
4.1%

Most occurring characters

ValueCountFrequency (%)
13836
14.9%
K 11024
11.9%
0 11024
11.9%
$ 11024
11.9%
s 5624
 
6.1%
n 5479
 
5.9%
4 4265
 
4.6%
- 3812
 
4.1%
e 2812
 
3.0%
L 2812
 
3.0%
Other values (12) 21164
22.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 25018
26.9%
Decimal Number 23873
25.7%
Uppercase Letter 14725
15.9%
Space Separator 13836
14.9%
Currency Symbol 11024
11.9%
Dash Punctuation 3812
 
4.1%
Math Symbol 588
 
0.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 5624
22.5%
n 5479
21.9%
e 2812
11.2%
a 2812
11.2%
h 2812
11.2%
t 2812
11.2%
k 889
 
3.6%
o 889
 
3.6%
w 889
 
3.6%
Decimal Number
ValueCountFrequency (%)
0 11024
46.2%
4 4265
 
17.9%
6 2575
 
10.8%
8 2359
 
9.9%
1 1825
 
7.6%
2 1825
 
7.6%
Uppercase Letter
ValueCountFrequency (%)
K 11024
74.9%
L 2812
 
19.1%
U 889
 
6.0%
Space Separator
ValueCountFrequency (%)
13836
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 11024
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3812
100.0%
Math Symbol
ValueCountFrequency (%)
+ 588
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 53133
57.2%
Latin 39743
42.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
K 11024
27.7%
s 5624
14.2%
n 5479
13.8%
e 2812
 
7.1%
L 2812
 
7.1%
a 2812
 
7.1%
h 2812
 
7.1%
t 2812
 
7.1%
U 889
 
2.2%
k 889
 
2.2%
Other values (2) 1778
 
4.5%
Common
ValueCountFrequency (%)
13836
26.0%
0 11024
20.7%
$ 11024
20.7%
4 4265
 
8.0%
- 3812
 
7.2%
6 2575
 
4.8%
8 2359
 
4.4%
1 1825
 
3.4%
2 1825
 
3.4%
+ 588
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 92876
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
13836
14.9%
K 11024
11.9%
0 11024
11.9%
$ 11024
11.9%
s 5624
 
6.1%
n 5479
 
5.9%
4 4265
 
4.6%
- 3812
 
4.1%
e 2812
 
3.0%
L 2812
 
3.0%
Other values (12) 21164
22.8%

Card_Category
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size63.4 KiB
Blue
7557 
Silver
 
436
Gold
 
93
Platinum
 
15

Length

Max length8
Median length4
Mean length4.1150475
Min length4

Characters and Unicode

Total characters33336
Distinct characters16
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBlue
2nd rowBlue
3rd rowGold
4th rowBlue
5th rowBlue

Common Values

ValueCountFrequency (%)
Blue 7557
93.3%
Silver 436
 
5.4%
Gold 93
 
1.1%
Platinum 15
 
0.2%

Length

2023-04-12T11:42:58.976037image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-12T11:42:59.144351image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
blue 7557
93.3%
silver 436
 
5.4%
gold 93
 
1.1%
platinum 15
 
0.2%

Most occurring characters

ValueCountFrequency (%)
l 8101
24.3%
e 7993
24.0%
u 7572
22.7%
B 7557
22.7%
i 451
 
1.4%
S 436
 
1.3%
v 436
 
1.3%
r 436
 
1.3%
G 93
 
0.3%
o 93
 
0.3%
Other values (6) 168
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 25235
75.7%
Uppercase Letter 8101
 
24.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 8101
32.1%
e 7993
31.7%
u 7572
30.0%
i 451
 
1.8%
v 436
 
1.7%
r 436
 
1.7%
o 93
 
0.4%
d 93
 
0.4%
a 15
 
0.1%
t 15
 
0.1%
Other values (2) 30
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
B 7557
93.3%
S 436
 
5.4%
G 93
 
1.1%
P 15
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 33336
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 8101
24.3%
e 7993
24.0%
u 7572
22.7%
B 7557
22.7%
i 451
 
1.4%
S 436
 
1.3%
v 436
 
1.3%
r 436
 
1.3%
G 93
 
0.3%
o 93
 
0.3%
Other values (6) 168
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 33336
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 8101
24.3%
e 7993
24.0%
u 7572
22.7%
B 7557
22.7%
i 451
 
1.4%
S 436
 
1.3%
v 436
 
1.3%
r 436
 
1.3%
G 93
 
0.3%
o 93
 
0.3%
Other values (6) 168
 
0.5%

Months_on_book
Real number (ℝ)

Distinct44
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.92359
Minimum13
Maximum56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size63.4 KiB
2023-04-12T11:42:59.294201image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile22
Q131
median36
Q340
95-th percentile50
Maximum56
Range43
Interquartile range (IQR)9

Descriptive statistics

Standard deviation8.0243588
Coefficient of variation (CV)0.22337297
Kurtosis0.35746635
Mean35.92359
Median Absolute Deviation (MAD)4
Skewness-0.10943003
Sum291017
Variance64.390334
MonotonicityNot monotonic
2023-04-12T11:42:59.455182image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
36 1950
24.1%
39 276
 
3.4%
37 276
 
3.4%
38 274
 
3.4%
40 269
 
3.3%
34 267
 
3.3%
35 256
 
3.2%
31 255
 
3.1%
33 250
 
3.1%
41 243
 
3.0%
Other values (34) 3785
46.7%
ValueCountFrequency (%)
13 57
0.7%
14 13
 
0.2%
15 28
 
0.3%
16 20
 
0.2%
17 31
 
0.4%
18 46
0.6%
19 54
0.7%
20 63
0.8%
21 71
0.9%
22 83
1.0%
ValueCountFrequency (%)
56 78
1.0%
55 33
 
0.4%
54 43
 
0.5%
53 63
0.8%
52 52
 
0.6%
51 64
0.8%
50 87
1.1%
49 114
1.4%
48 133
1.6%
47 139
1.7%

Total_Relationship_Count
Real number (ℝ)

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8132329
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size63.4 KiB
2023-04-12T11:42:59.584385image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median4
Q35
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.5518377
Coefficient of variation (CV)0.40696115
Kurtosis-0.99950613
Mean3.8132329
Median Absolute Deviation (MAD)1
Skewness-0.16312656
Sum30891
Variance2.4082002
MonotonicityNot monotonic
2023-04-12T11:42:59.716605image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 1852
22.9%
4 1539
19.0%
5 1511
18.7%
6 1488
18.4%
2 985
12.2%
1 726
 
9.0%
ValueCountFrequency (%)
1 726
 
9.0%
2 985
12.2%
3 1852
22.9%
4 1539
19.0%
5 1511
18.7%
6 1488
18.4%
ValueCountFrequency (%)
6 1488
18.4%
5 1511
18.7%
4 1539
19.0%
3 1852
22.9%
2 985
12.2%
1 726
 
9.0%

Months_Inactive_12_mon
Real number (ℝ)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3468708
Minimum0
Maximum6
Zeros22
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size63.4 KiB
2023-04-12T11:42:59.844812image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q33
95-th percentile4
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.0141769
Coefficient of variation (CV)0.43214006
Kurtosis1.1291968
Mean2.3468708
Median Absolute Deviation (MAD)1
Skewness0.64425849
Sum19012
Variance1.0285547
MonotonicityNot monotonic
2023-04-12T11:42:59.960997image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3 3094
38.2%
2 2611
32.2%
1 1780
22.0%
4 346
 
4.3%
5 144
 
1.8%
6 104
 
1.3%
0 22
 
0.3%
ValueCountFrequency (%)
0 22
 
0.3%
1 1780
22.0%
2 2611
32.2%
3 3094
38.2%
4 346
 
4.3%
5 144
 
1.8%
6 104
 
1.3%
ValueCountFrequency (%)
6 104
 
1.3%
5 144
 
1.8%
4 346
 
4.3%
3 3094
38.2%
2 2611
32.2%
1 1780
22.0%
0 22
 
0.3%

Contacts_Count_12_mon
Real number (ℝ)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4503148
Minimum0
Maximum6
Zeros312
Zeros (%)3.9%
Negative0
Negative (%)0.0%
Memory size63.4 KiB
2023-04-12T11:43:00.101958image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q33
95-th percentile4
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1006873
Coefficient of variation (CV)0.44920241
Kurtosis0.029614465
Mean2.4503148
Median Absolute Deviation (MAD)1
Skewness0.020659003
Sum19850
Variance1.2115126
MonotonicityNot monotonic
2023-04-12T11:43:00.228605image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3 2716
33.5%
2 2596
32.0%
1 1207
14.9%
4 1092
13.5%
0 312
 
3.9%
5 133
 
1.6%
6 45
 
0.6%
ValueCountFrequency (%)
0 312
 
3.9%
1 1207
14.9%
2 2596
32.0%
3 2716
33.5%
4 1092
13.5%
5 133
 
1.6%
6 45
 
0.6%
ValueCountFrequency (%)
6 45
 
0.6%
5 133
 
1.6%
4 1092
13.5%
3 2716
33.5%
2 2596
32.0%
1 1207
14.9%
0 312
 
3.9%

Credit_Limit
Real number (ℝ)

Distinct5325
Distinct (%)65.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8636.5481
Minimum1438.3
Maximum34516
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size63.4 KiB
2023-04-12T11:43:00.405818image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1438.3
5-th percentile1438.3
Q12555
median4549
Q311128
95-th percentile34058
Maximum34516
Range33077.7
Interquartile range (IQR)8573

Descriptive statistics

Standard deviation9086.4196
Coefficient of variation (CV)1.0520893
Kurtosis1.777607
Mean8636.5481
Median Absolute Deviation (MAD)2597
Skewness1.6576093
Sum69964676
Variance82563020
MonotonicityNot monotonic
2023-04-12T11:43:00.594420image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1438.3 413
 
5.1%
34516 399
 
4.9%
9959 15
 
0.2%
15987 14
 
0.2%
23981 9
 
0.1%
6224 8
 
0.1%
2490 8
 
0.1%
3735 7
 
0.1%
7469 6
 
0.1%
1963 6
 
0.1%
Other values (5315) 7216
89.1%
ValueCountFrequency (%)
1438.3 413
5.1%
1439 2
 
< 0.1%
1440 1
 
< 0.1%
1441 1
 
< 0.1%
1442 1
 
< 0.1%
1443 3
 
< 0.1%
1446 1
 
< 0.1%
1449 1
 
< 0.1%
1452 2
 
< 0.1%
1454 1
 
< 0.1%
ValueCountFrequency (%)
34516 399
4.9%
34496 1
 
< 0.1%
34458 1
 
< 0.1%
34427 1
 
< 0.1%
34198 1
 
< 0.1%
34173 1
 
< 0.1%
34162 1
 
< 0.1%
34058 1
 
< 0.1%
33996 1
 
< 0.1%
33951 1
 
< 0.1%

Total_Revolving_Bal
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1883
Distinct (%)23.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1160.3828
Minimum0
Maximum2517
Zeros1986
Zeros (%)24.5%
Negative0
Negative (%)0.0%
Memory size63.4 KiB
2023-04-12T11:43:00.790726image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1326
median1273
Q31782
95-th percentile2517
Maximum2517
Range2517
Interquartile range (IQR)1456

Descriptive statistics

Standard deviation815.50429
Coefficient of variation (CV)0.70278903
Kurtosis-1.1491884
Mean1160.3828
Median Absolute Deviation (MAD)590
Skewness-0.1444843
Sum9400261
Variance665047.25
MonotonicityNot monotonic
2023-04-12T11:43:00.976542image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1986
 
24.5%
2517 415
 
5.1%
1965 11
 
0.1%
1720 11
 
0.1%
1434 10
 
0.1%
1176 9
 
0.1%
1566 9
 
0.1%
1010 9
 
0.1%
1384 9
 
0.1%
1647 9
 
0.1%
Other values (1873) 5623
69.4%
ValueCountFrequency (%)
0 1986
24.5%
134 1
 
< 0.1%
145 1
 
< 0.1%
154 1
 
< 0.1%
157 1
 
< 0.1%
159 1
 
< 0.1%
168 1
 
< 0.1%
170 1
 
< 0.1%
186 1
 
< 0.1%
193 2
 
< 0.1%
ValueCountFrequency (%)
2517 415
5.1%
2514 3
 
< 0.1%
2513 1
 
< 0.1%
2512 2
 
< 0.1%
2511 1
 
< 0.1%
2509 1
 
< 0.1%
2508 1
 
< 0.1%
2507 3
 
< 0.1%
2506 1
 
< 0.1%
2505 2
 
< 0.1%

Avg_Open_To_Buy
Real number (ℝ)

Distinct5757
Distinct (%)71.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7476.1653
Minimum3
Maximum34516
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size63.4 KiB
2023-04-12T11:43:01.158985image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile489
Q11341
median3495
Q39942
95-th percentile32099
Maximum34516
Range34513
Interquartile range (IQR)8601

Descriptive statistics

Standard deviation9080.2799
Coefficient of variation (CV)1.2145638
Kurtosis1.7726129
Mean7476.1653
Median Absolute Deviation (MAD)2674
Skewness1.6540051
Sum60564415
Variance82451483
MonotonicityNot monotonic
2023-04-12T11:43:01.335096image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1438.3 261
 
3.2%
34516 81
 
1.0%
31999 21
 
0.3%
447 6
 
0.1%
787 6
 
0.1%
1129 6
 
0.1%
953 6
 
0.1%
990 6
 
0.1%
837 6
 
0.1%
933 6
 
0.1%
Other values (5747) 7696
95.0%
ValueCountFrequency (%)
3 1
< 0.1%
10 1
< 0.1%
14 1
< 0.1%
15 1
< 0.1%
24 1
< 0.1%
28 1
< 0.1%
36 1
< 0.1%
39 2
< 0.1%
41 2
< 0.1%
42 1
< 0.1%
ValueCountFrequency (%)
34516 81
1.0%
34362 1
 
< 0.1%
34300 1
 
< 0.1%
34297 1
 
< 0.1%
34286 1
 
< 0.1%
34238 1
 
< 0.1%
34227 1
 
< 0.1%
34119 1
 
< 0.1%
34117 1
 
< 0.1%
34084 1
 
< 0.1%

Total_Amt_Chng_Q4_Q1
Real number (ℝ)

Distinct1089
Distinct (%)13.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.76080879
Minimum0
Maximum2.675
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size63.4 KiB
2023-04-12T11:43:01.526621image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.466
Q10.632
median0.738
Q30.859
95-th percentile1.106
Maximum2.675
Range2.675
Interquartile range (IQR)0.227

Descriptive statistics

Standard deviation0.21666781
Coefficient of variation (CV)0.28478615
Kurtosis6.6213016
Mean0.76080879
Median Absolute Deviation (MAD)0.113
Skewness1.4913244
Sum6163.312
Variance0.046944938
MonotonicityNot monotonic
2023-04-12T11:43:01.714790image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.767 28
 
0.3%
0.718 27
 
0.3%
0.76 27
 
0.3%
0.743 27
 
0.3%
0.725 26
 
0.3%
0.742 26
 
0.3%
0.69 26
 
0.3%
0.722 26
 
0.3%
0.791 26
 
0.3%
0.717 26
 
0.3%
Other values (1079) 7836
96.7%
ValueCountFrequency (%)
0 4
< 0.1%
0.01 1
 
< 0.1%
0.046 1
 
< 0.1%
0.061 2
< 0.1%
0.101 1
 
< 0.1%
0.12 1
 
< 0.1%
0.153 1
 
< 0.1%
0.163 1
 
< 0.1%
0.166 1
 
< 0.1%
0.175 1
 
< 0.1%
ValueCountFrequency (%)
2.675 1
< 0.1%
2.594 1
< 0.1%
2.368 1
< 0.1%
2.316 1
< 0.1%
2.282 1
< 0.1%
2.271 1
< 0.1%
2.204 1
< 0.1%
2.175 1
< 0.1%
2.145 1
< 0.1%
2.121 1
< 0.1%

Total_Trans_Amt
Real number (ℝ)

Distinct4462
Distinct (%)55.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4402.9881
Minimum510
Maximum18484
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size63.4 KiB
2023-04-12T11:43:01.892990image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum510
5-th percentile1284
Q12160
median3897
Q34739
95-th percentile14215
Maximum18484
Range17974
Interquartile range (IQR)2579

Descriptive statistics

Standard deviation3401.7095
Coefficient of variation (CV)0.77259112
Kurtosis3.904927
Mean4402.9881
Median Absolute Deviation (MAD)1298
Skewness2.0479515
Sum35668607
Variance11571628
MonotonicityNot monotonic
2023-04-12T11:43:02.068447image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4220 9
 
0.1%
4518 9
 
0.1%
4498 9
 
0.1%
4317 8
 
0.1%
4509 8
 
0.1%
4869 8
 
0.1%
4833 8
 
0.1%
4037 7
 
0.1%
1409 7
 
0.1%
4503 7
 
0.1%
Other values (4452) 8021
99.0%
ValueCountFrequency (%)
510 1
< 0.1%
563 1
< 0.1%
569 1
< 0.1%
594 1
< 0.1%
596 1
< 0.1%
597 1
< 0.1%
602 1
< 0.1%
615 1
< 0.1%
644 1
< 0.1%
647 2
< 0.1%
ValueCountFrequency (%)
18484 1
< 0.1%
17995 1
< 0.1%
17744 1
< 0.1%
17634 1
< 0.1%
17628 1
< 0.1%
17498 1
< 0.1%
17437 1
< 0.1%
17390 1
< 0.1%
17350 1
< 0.1%
17258 1
< 0.1%

Total_Trans_Ct
Real number (ℝ)

Distinct126
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.907789
Minimum10
Maximum139
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size63.4 KiB
2023-04-12T11:43:02.256518image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile28
Q145
median67
Q381
95-th percentile105
Maximum139
Range129
Interquartile range (IQR)36

Descriptive statistics

Standard deviation23.556379
Coefficient of variation (CV)0.36292068
Kurtosis-0.37133836
Mean64.907789
Median Absolute Deviation (MAD)17
Skewness0.15361702
Sum525818
Variance554.90298
MonotonicityNot monotonic
2023-04-12T11:43:02.584560image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
77 168
 
2.1%
82 168
 
2.1%
81 164
 
2.0%
74 160
 
2.0%
75 160
 
2.0%
76 159
 
2.0%
69 159
 
2.0%
70 158
 
2.0%
73 157
 
1.9%
71 157
 
1.9%
Other values (116) 6491
80.1%
ValueCountFrequency (%)
10 1
 
< 0.1%
11 2
 
< 0.1%
12 4
 
< 0.1%
13 3
 
< 0.1%
14 8
 
0.1%
15 15
0.2%
16 9
0.1%
17 12
0.1%
18 21
0.3%
19 10
0.1%
ValueCountFrequency (%)
139 1
 
< 0.1%
138 1
 
< 0.1%
134 1
 
< 0.1%
132 1
 
< 0.1%
131 4
 
< 0.1%
130 4
 
< 0.1%
129 5
0.1%
128 10
0.1%
127 11
0.1%
126 6
0.1%

Total_Ct_Chng_Q4_Q1
Real number (ℝ)

Distinct795
Distinct (%)9.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.71217615
Minimum0
Maximum3.714
Zeros6
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size63.4 KiB
2023-04-12T11:43:02.767050image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.367
Q10.583
median0.702
Q30.818
95-th percentile1.069
Maximum3.714
Range3.714
Interquartile range (IQR)0.235

Descriptive statistics

Standard deviation0.23932079
Coefficient of variation (CV)0.33604157
Kurtosis16.555569
Mean0.71217615
Median Absolute Deviation (MAD)0.117
Skewness2.1268648
Sum5769.339
Variance0.057274443
MonotonicityNot monotonic
2023-04-12T11:43:02.939988image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.667 141
 
1.7%
1 128
 
1.6%
0.75 128
 
1.6%
0.5 119
 
1.5%
0.6 95
 
1.2%
0.8 86
 
1.1%
0.714 77
 
1.0%
0.833 75
 
0.9%
0.778 57
 
0.7%
0.625 50
 
0.6%
Other values (785) 7145
88.2%
ValueCountFrequency (%)
0 6
0.1%
0.028 1
 
< 0.1%
0.029 1
 
< 0.1%
0.038 1
 
< 0.1%
0.053 1
 
< 0.1%
0.059 2
 
< 0.1%
0.062 1
 
< 0.1%
0.074 1
 
< 0.1%
0.077 1
 
< 0.1%
0.091 3
< 0.1%
ValueCountFrequency (%)
3.714 1
 
< 0.1%
3.571 1
 
< 0.1%
3.5 1
 
< 0.1%
3 2
< 0.1%
2.875 1
 
< 0.1%
2.75 1
 
< 0.1%
2.571 1
 
< 0.1%
2.5 3
< 0.1%
2.429 1
 
< 0.1%
2.4 2
< 0.1%

Avg_Utilization_Ratio
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct943
Distinct (%)11.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.27318664
Minimum0
Maximum0.999
Zeros1986
Zeros (%)24.5%
Negative0
Negative (%)0.0%
Memory size63.4 KiB
2023-04-12T11:43:03.136911image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.022
median0.174
Q30.497
95-th percentile0.789
Maximum0.999
Range0.999
Interquartile range (IQR)0.475

Descriptive statistics

Standard deviation0.27459484
Coefficient of variation (CV)1.0051547
Kurtosis-0.77916141
Mean0.27318664
Median Absolute Deviation (MAD)0.174
Skewness0.72609436
Sum2213.085
Variance0.075402324
MonotonicityNot monotonic
2023-04-12T11:43:03.322425image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1986
 
24.5%
0.073 35
 
0.4%
0.057 26
 
0.3%
0.07 25
 
0.3%
0.048 25
 
0.3%
0.053 24
 
0.3%
0.06 24
 
0.3%
0.069 24
 
0.3%
0.061 23
 
0.3%
0.071 22
 
0.3%
Other values (933) 5887
72.7%
ValueCountFrequency (%)
0 1986
24.5%
0.004 1
 
< 0.1%
0.006 2
 
< 0.1%
0.007 1
 
< 0.1%
0.008 2
 
< 0.1%
0.009 1
 
< 0.1%
0.01 1
 
< 0.1%
0.011 1
 
< 0.1%
0.012 3
 
< 0.1%
0.013 2
 
< 0.1%
ValueCountFrequency (%)
0.999 1
 
< 0.1%
0.995 1
 
< 0.1%
0.994 1
 
< 0.1%
0.99 1
 
< 0.1%
0.987 1
 
< 0.1%
0.985 1
 
< 0.1%
0.984 1
 
< 0.1%
0.983 4
< 0.1%
0.978 1
 
< 0.1%
0.977 1
 
< 0.1%

Attrition_Flag
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size63.4 KiB
1
6801 
0
1300 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8101
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 6801
84.0%
0 1300
 
16.0%

Length

2023-04-12T11:43:03.476109image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-12T11:43:03.621921image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1 6801
84.0%
0 1300
 
16.0%

Most occurring characters

ValueCountFrequency (%)
1 6801
84.0%
0 1300
 
16.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8101
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 6801
84.0%
0 1300
 
16.0%

Most occurring scripts

ValueCountFrequency (%)
Common 8101
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 6801
84.0%
0 1300
 
16.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8101
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 6801
84.0%
0 1300
 
16.0%

Interactions

2023-04-12T11:42:52.539058image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:13.065237image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:15.750686image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:18.342337image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:21.468054image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:24.434041image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:27.131035image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:29.620512image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:32.166063image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:34.794345image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:37.306783image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:39.737799image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:42.416699image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:44.865397image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:47.409773image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:49.876025image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:52.706331image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:13.231037image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:15.936953image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:18.547348image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:21.642926image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:24.613800image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:27.297670image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:29.798089image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:32.332178image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:34.959893image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:37.466373image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:39.907226image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:42.581285image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:45.030912image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:47.568475image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:50.051960image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:52.858458image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:13.461356image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:16.100269image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:18.731609image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:21.799803image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:24.773395image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:27.448359image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:29.967892image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:32.625885image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:35.112653image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:37.617156image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:40.065540image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:42.730003image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:45.198318image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:47.720425image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:50.209900image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:53.004058image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:13.593086image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:16.243829image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:18.886554image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:21.967086image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:24.928708image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:27.592688image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:30.123877image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:32.763489image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:35.259731image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:37.757396image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:40.215589image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:42.876618image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:45.345990image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:47.860036image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:50.507415image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:53.146028image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:13.728125image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:16.393099image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:19.051889image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:22.143260image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:25.086995image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:27.740129image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:30.283219image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:32.904503image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:35.407374image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:37.908544image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:40.371417image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:43.022143image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:45.502764image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:48.009928image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:50.651451image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:53.278597image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:13.852170image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:16.535256image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:19.198962image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:22.328997image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:25.272402image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:27.876331image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:30.423148image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:33.040958image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:35.547115image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:38.045089image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:40.513097image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:43.157880image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:45.647399image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:48.153098image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:50.785408image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:53.424727image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:13.989939image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:16.687802image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:19.357301image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:22.505982image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:25.499802image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:28.026513image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:30.582129image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:33.192625image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:35.698945image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:38.194027image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:40.671858image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:43.307064image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:45.803863image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:48.308676image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:50.933556image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:53.580916image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:14.135319image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:16.841102image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:19.532956image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:22.694575image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:25.695365image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:28.181357image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:30.737404image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:33.347099image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:35.855535image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:38.347102image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:40.831181image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:43.461902image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:45.961009image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:48.466713image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:51.088278image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:53.733128image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:14.302599image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:16.996759image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:19.869983image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:22.890383image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:25.872874image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:28.333062image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:30.895006image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:33.500487image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:36.018736image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:38.498759image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:40.991336image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:43.617036image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:46.122877image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:48.625603image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:51.251436image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:53.892230image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:14.502678image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:17.156393image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:20.221807image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:23.070792image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:26.041656image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:28.495729image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:31.057349image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:33.684125image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:36.184930image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:38.660041image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:41.158123image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:43.781010image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:46.285721image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:48.789858image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:51.420321image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:54.038272image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:14.670335image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:17.302656image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:20.428250image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:23.382981image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:26.200126image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:28.645782image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:31.207724image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:33.830974image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:36.343693image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:38.802445image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:41.447936image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:43.927944image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:46.436358image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:48.939726image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:51.573923image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:54.204597image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:14.852569image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:17.464903image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:20.635774image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:23.556402image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:26.361121image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:28.810344image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:31.366644image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:33.988360image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:36.508387image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:38.961631image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:41.604919image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:44.093952image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:46.601357image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:49.101520image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:51.740879image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:54.355786image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:15.036687image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:17.624493image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:20.801816image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:23.728101image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:26.515214image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:28.957741image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:31.518797image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:34.143963image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:36.666936image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:39.116815image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:41.758519image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:44.239860image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:46.755897image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:49.247660image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:51.889685image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:54.518364image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:15.219083image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:17.792360image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:20.978737image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:23.915463image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:26.676730image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:29.126912image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:31.683887image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:34.311744image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:36.830884image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:39.281128image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:41.925969image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:44.400023image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:46.913298image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:49.411139image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:52.054432image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:54.675869image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:15.399985image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:17.977387image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:21.140212image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:24.084934image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:26.825927image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:29.292007image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:31.853761image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:34.466481image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:36.988982image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:39.432700image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:42.090297image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:44.558143image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:47.073729image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:49.560916image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:52.213596image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:54.832220image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:15.576848image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:18.166557image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:21.314082image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:24.261081image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:26.985333image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:29.458865image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:32.014879image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:34.628736image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:37.154716image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:39.592706image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:42.260037image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:44.715312image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:47.238249image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:49.726286image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-04-12T11:42:52.383042image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2023-04-12T11:43:03.766794image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
train_idxCLIENTNUMCustomer_AgeDependent_countMonths_on_bookTotal_Relationship_CountMonths_Inactive_12_monContacts_Count_12_monCredit_LimitTotal_Revolving_BalAvg_Open_To_BuyTotal_Amt_Chng_Q4_Q1Total_Trans_AmtTotal_Trans_CtTotal_Ct_Chng_Q4_Q1Avg_Utilization_RatioGenderEducation_LevelMarital_StatusIncome_CategoryCard_CategoryAttrition_Flag
train_idx1.0000.0020.002-0.0040.015-0.0090.0030.001-0.006-0.005-0.0030.0050.0140.0180.0090.0020.0070.0070.0000.0000.0000.000
CLIENTNUM0.0021.0000.017-0.0200.1110.017-0.0090.0100.015-0.0010.0140.028-0.0020.0050.0090.0030.0000.0150.0000.0000.0000.045
Customer_Age0.0020.0171.000-0.1390.773-0.0120.039-0.000-0.0030.011-0.006-0.077-0.036-0.057-0.0320.0100.0000.0240.0910.0720.0240.037
Dependent_count-0.004-0.020-0.1391.000-0.115-0.0310.001-0.0480.0560.0030.056-0.0280.0510.0430.001-0.0320.0090.0140.0400.0440.0130.014
Months_on_book0.0150.1110.773-0.1151.000-0.0110.0580.0030.0030.0100.004-0.063-0.030-0.045-0.032-0.0020.0000.0000.0500.0480.0030.015
Total_Relationship_Count-0.0090.017-0.012-0.031-0.0111.000-0.0150.060-0.0590.011-0.0710.028-0.271-0.2200.0190.0650.0200.0000.0230.0130.0680.164
Months_Inactive_12_mon0.003-0.0090.0390.0010.058-0.0151.0000.031-0.033-0.041-0.022-0.026-0.028-0.046-0.051-0.0210.0200.0000.0080.0180.0000.203
Contacts_Count_12_mon0.0010.010-0.000-0.0480.0030.0600.0311.0000.024-0.0500.038-0.019-0.176-0.177-0.096-0.0680.0630.0000.0000.0230.0000.246
Credit_Limit-0.0060.015-0.0030.0560.003-0.059-0.0330.0241.0000.1420.9320.0150.0240.033-0.016-0.4110.4430.0000.0300.2780.3350.038
Total_Revolving_Bal-0.005-0.0010.0110.0030.0100.011-0.041-0.0500.1421.000-0.1430.0330.0240.0500.0820.7050.0330.0160.0100.0210.0230.403
Avg_Open_To_Buy-0.0030.014-0.0060.0560.004-0.071-0.0220.0380.932-0.1431.0000.0020.0150.017-0.045-0.6810.4450.0000.0300.2780.3360.024
Total_Amt_Chng_Q4_Q10.0050.028-0.077-0.028-0.0630.028-0.026-0.0190.0150.0330.0021.0000.1280.0770.2910.0330.0650.0170.0560.0300.0120.229
Total_Trans_Amt0.014-0.002-0.0360.051-0.030-0.271-0.028-0.1760.0240.0240.0150.1281.0000.8800.2280.0300.2500.0070.1050.0950.1590.325
Total_Trans_Ct0.0180.005-0.0570.043-0.045-0.220-0.046-0.1770.0330.0500.0170.0770.8801.0000.2350.0500.1660.0010.1000.0600.1100.464
Total_Ct_Chng_Q4_Q10.0090.009-0.0320.001-0.0320.019-0.051-0.096-0.0160.082-0.0450.2910.2280.2351.0000.0980.0480.0050.0310.0240.0000.317
Avg_Utilization_Ratio0.0020.0030.010-0.032-0.0020.065-0.021-0.068-0.4110.705-0.6810.0330.0300.0500.0981.0000.2790.0000.0270.1650.1450.242
Gender0.0070.0000.0000.0090.0000.0200.0200.0630.4430.0330.4450.0650.2500.1660.0480.2791.0000.0180.0170.8410.0810.046
Education_Level0.0070.0150.0240.0140.0000.0000.0000.0000.0000.0160.0000.0170.0070.0010.0050.0000.0181.0000.0160.0160.0200.038
Marital_Status0.0000.0000.0910.0400.0500.0230.0080.0000.0300.0100.0300.0560.1050.1000.0310.0270.0170.0161.0000.0150.0250.000
Income_Category0.0000.0000.0720.0440.0480.0130.0180.0230.2780.0210.2780.0300.0950.0600.0240.1650.8410.0160.0151.0000.0530.029
Card_Category0.0000.0000.0240.0130.0030.0680.0000.0000.3350.0230.3360.0120.1590.1100.0000.1450.0810.0200.0250.0531.0000.016
Attrition_Flag0.0000.0450.0370.0140.0150.1640.2030.2460.0380.4030.0240.2290.3250.4640.3170.2420.0460.0380.0000.0290.0161.000

Missing values

2023-04-12T11:42:55.089778image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-12T11:42:55.590078image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

train_idxCLIENTNUMCustomer_AgeGenderDependent_countEducation_LevelMarital_StatusIncome_CategoryCard_CategoryMonths_on_bookTotal_Relationship_CountMonths_Inactive_12_monContacts_Count_12_monCredit_LimitTotal_Revolving_BalAvg_Open_To_BuyTotal_Amt_Chng_Q4_Q1Total_Trans_AmtTotal_Trans_CtTotal_Ct_Chng_Q4_Q1Avg_Utilization_RatioAttrition_Flag
0071307138354F1UnknownSingleUnknownBlue361333723.017281995.00.5958554990.6780.4641
1171424633358F4High SchoolMarriedUnknownBlue481435396.018033593.00.4932107390.3930.3340
2271820678345F4UnknownSingleLess than $40KGold3661315987.0164814339.00.7321436361.2500.1031
3372109698334F2GraduateSingleLess than $40KBlue364343625.025171108.01.1582616461.3000.6941
4472002868349F2High SchoolMarried$40K - $60KBlue395342720.01926794.00.6023806610.7940.7081
5577894223360F0DoctorateMarriedLess than $40KBlue455241438.3648790.30.4771267271.0770.4511
6670868290843F4UnknownSingleUnknownBlue282212838.01934904.00.8738644870.5540.6811
7772067045852F2UnknownSingle$40K - $60KBlue453133476.015601916.00.8943496580.8710.4491
8871995240830M0GraduateMarriedLess than $40KBlue363322550.01623927.00.6501870510.2750.6361
9970841275833F3GraduateSingleLess than $40KBlue365231457.001457.00.6772200450.3640.0000
train_idxCLIENTNUMCustomer_AgeGenderDependent_countEducation_LevelMarital_StatusIncome_CategoryCard_CategoryMonths_on_bookTotal_Relationship_CountMonths_Inactive_12_monContacts_Count_12_monCredit_LimitTotal_Revolving_BalAvg_Open_To_BuyTotal_Amt_Chng_Q4_Q1Total_Trans_AmtTotal_Trans_CtTotal_Ct_Chng_Q4_Q1Avg_Utilization_RatioAttrition_Flag
8091809171482540845F3High SchoolSingle$40K - $60KBlue362332853.02517336.00.5954971650.7570.8821
8092809281246595853F3Post-GraduateDivorcedUnknownBlue4852212286.099711289.00.7264960830.7290.0811
8093809370927458340M2High SchoolMarried$120K +Blue2753112248.0132310925.00.8824806910.8960.1081
8094809470821775863M2GraduateMarried$60K - $80KBlue4952314035.0206111974.02.2711606301.5000.1471
8095809571814835850F3High SchoolMarriedLess than $40KBlue362331572.001572.00.7402447410.5770.0000
8096809676905303344F1GraduateSingle$40K - $60KBlue383254142.025171625.00.8092104440.8330.6080
8097809771440615853F3High SchoolDivorcedUnknownBlue364367939.007939.00.5512269420.3120.0000
8098809871414013342F4GraduateUnknownLess than $40KBlue323122314.01547767.00.8044678741.0000.6691
8099809972024498340M3UnknownSingle$40K - $60KBlue284113563.017071856.00.5061482420.3120.4791
8100810082712388353M4High SchoolSingle$60K - $80KBlue495123858.003858.00.6704472920.6140.0001